Hi Sunny,

Welcome to Flink 😊.

The next thing for you to consider is to setup checkpointing [1] which allows a 
failing job to pick up from where it stopped.

Sincere greetings from the supposed close-by  Zurich 😊

Thias


[1] 
https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/fault-tolerance/checkpointing/


From: Raihan Sunny <raihan.su...@selise.ch>
Sent: Tuesday, December 20, 2022 6:30 AM
To: user@flink.apache.org
Subject: Understanding pipelined regions

⚠EXTERNAL MESSAGE – CAUTION: Think Before You Click ⚠


Hi,

I'm quite new to the world of stream and batch processing. I've been reading 
about pipelined regions in Flink and am quite confused by what it means. My 
specific problem involves a streaming job that looks like the following:

1. There is a Kafka source that takes in an input data that sets off a series 
of operations
2. As part of the first operation, I have an operator that produces multiple 
values, each of which has to be fed into several different operators in parallel
3. The operators each produce a result which I keyBy and merge together using 
the union operator
4. The merged result is then written to a Kafka sink

The problem is that when one of the parallel operators throws an exception, all 
the tasks in the entire pipeline gets restarted including the source which then 
replays the input data and the process starts off once again. My question is if 
it's possible to make the tasks of only the branch that failed restart rather 
than the whole job. I do realize that it is possible to split up the job such 
that the first operator produces its output to a sink and having that as the 
source to the subsequent operations can mitigate the problem. I was just 
wondering if it's possible in the scenario that I have described above. In 
general, how can I "create" a pipelined region?


Thanks,
Sunny


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